Advertisement

The Simulation of the Indoor Positioning by Panoramic Camera and Point Cloud Scanner

  • Jiun-Jian Liaw
  • Kun-Leng Chen
  • Tzu-Cheng Huang
  • Yu-Huei Cheng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 513)

Abstract

Positioning system is more and more important but it is not easy to have indoor positioning without wireless signal. In this paper, we proposed a positioning method with digital panorama image and point cloud system. The point cloud system is used to scan the indoor environment, and filter out the feature objects with the coordinates. The panoramic camera is used to shoot the panoramic image and to find the location of feature objects in the environment. The proposed method calculates the position with the image which taken from panoramic camera and the coordinates from the point cloud system. According to the experimental results, the error in the simulation indoor space is less than 30 mm.

Keywords

Indoor positioning Panoramic image Point cloud system Raspberry Pi Fish-eye lens 

Notes

Acknowledgements

This study is partial supported by “MOST 105-2221-E-324-008-MY2”, Taiwan, Republic of China.

References

  1. 1.
    Leu JS, Tzeng HJ (2012) Received signal strength fingerprint and footprint assisted indoor positioning based on ambient Wi-Fi signals. In: 2012 IEEE 75th vehicular technology conference (VTC Spring), Yokohama, Japan, 6–9 May 2012Google Scholar
  2. 2.
    Cherubini A, Spindler F, Chaumette F (2012) A new tentacles-based technique for avoiding obstacles during visual navigation. In: 2012 IEEE international conference on robotics and automation, Saint, Paul, MU, USA, 14–18 May 2012Google Scholar
  3. 3.
    Wilk P, Karciarz J, (2014) Optimization of map matching algorithms for indoor navigation in shopping malls. In: 2014 international conference on indoor positioning and indoor navigation (IPIN), Busan, South Korea, 27–30 Oct 2014Google Scholar
  4. 4.
    Ma L, Li J, Xu Y (2015) Radio map recovery and noise reduction method for green WiFi indoor positioning system based on inexact augmented lagrange multiplier algorithm. In: 2015 IEEE global communications conference (GLOBECOM), San Diego, CA, USA, 6–10 Dec 2015Google Scholar
  5. 5.
    Grisso R, Alley M, Heatwole (2009) Precision farming tools: Global positioning system (GPS). In: Victorian certificate of education (VCE) publications, no 442, pp 442–503Google Scholar
  6. 6.
    Chen Z, Zhu Q, Jiang H (2015) Indoor localization using smartphone sensors and iBeacons. In: 2015 IEEE 10th conference on industrial electronics and applications (ICIEA), Auckland, New Zealand, pp 1723–1728, 15–17 June 2015Google Scholar
  7. 7.
    Abdullah A, Ma Y, Tafazolli R (2014) Survey of state-of-the-art fingerprinting technique for in-door radio positioning. In: Communications surveys and tutorials, pp 97–106, Oct 2014Google Scholar
  8. 8.
    Watcharasukchit S, Triyason T, Krathut W, Tassanaviboon A, Arpnikanondt C (2017) WiFi indoor positioning with binary search method. In: International conference on Ubi-media computing and workshops (Ubi-Media), Pattaya, Thailand, 1–4 Aug 2017Google Scholar
  9. 9.
    Chen Z, Zhu Q, Soh YC (2016) Smartphone inertial sensor-based indoor localization and tracking with iBeacon corrections. IEEE Trans Industr Inf 12(4):1540–1549CrossRefGoogle Scholar
  10. 10.
    Li Z, Yang Y, Pahlacan K (2016) Using iBeacon for newborns localization in hospitals. In: International conference on medical information and communication technology, Worcester, MA, USA, 20–23 Mar 2016Google Scholar
  11. 11.
    Wang C, Shi Z, Wu F, Zhang J (2016) An RFID indoor positioning system by using particle swarm optimization-based artificial neural network. In: International conference on audio, language and image processing, Shanghai, China, 11–12 July 2016Google Scholar
  12. 12.
    Wang J, Wang Y, Guan X (2016) An indoor localization system based on backscatter RFID tag. In: IEEE wireless communications and networking conference, Doha, Qatar, 3–6 Apr 2016Google Scholar
  13. 13.
    Liu Y, Qiu Y (2012) An indoor localization of UHF RFID using a hybrid approach. In: International conference on consumer electronics, communications and networks, Yichang, China, 21–23 Apr 2012Google Scholar
  14. 14.
    Xia H, Huang T, Chen G (2015) A method of extracting human facial feature points based on 3D laser scanning point cloud data. In: 2015 23rd international conference on geoinformatics, pp 1–3, Wuhan, China, 19–21 June 2015Google Scholar
  15. 15.
    You H, Zhang S (2006) Reconstructing 3D buildings based on airborne CCD image and laser scanning range-finder data. Opt Precis Eng 14(2):297–302Google Scholar
  16. 16.
    Bu L, Zhang Z (2008) Application of point clouds from terrestrial 3D laser scanner for deformation measurements. In: The international archives of the photogrammetry, remote sensing and spatial information sciences, pp 545–548Google Scholar
  17. 17.
    Bassford M, Painter B (2015) Development of an intelligent Fisheye camera. In: 2015 international conference on IE, pp 160–163, Prague, Czech Republic, 15–17 July 2015Google Scholar
  18. 18.
    Zhang Q, Kamata SI (2015) Fisheye image correction based on straight-line detection and preservation. In: 2015 international conference on SMC, pp 1793–1797, Kowloon, Hong Kong, 9–12 Oct 2015Google Scholar
  19. 19.
    Xu J, Stenger G, Kerola T, Tung T (2017) Pano2CAD: room layout from a single panorama image. In: 2017 IEEE winter conference on WACV, Santa Rosa, CA, USA, 15 May 2017Google Scholar
  20. 20.
    Kweon GI, Choi YH (2010) Image-processing based panoramic camera employing single Fisheye lens. J Opt Soc Korea 14(3):245–259CrossRefGoogle Scholar
  21. 21.
    Ho T, Budagavi M (2017) Dual-fisheye lens stitching for 360-degree imaging. In: 2017 IEEE international conference on ICASSP, New Orleans, LA, USA, 19 June 2017Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jiun-Jian Liaw
    • 1
  • Kun-Leng Chen
    • 1
  • Tzu-Cheng Huang
    • 1
  • Yu-Huei Cheng
    • 1
  1. 1.Department of Information and Communication EngineeringChaoyang University of TechnologyTaichung CityRepublic of China

Personalised recommendations